Noise Monitoring at Canada’S Simonette Sour Oil Processing Facility
Bibliographic record
Abstract
Abstract Field testing of electrochemical noise monitoring has been conducted at the Simonette sour oil processing facility since August of 1997. Results from the installation at the Simonette sour oil processing facility are promising and have provided valuable insight into which process variables significantly affect corrosion rates and mechanisms in the system. Because electrochemical noise measurements allow monitoring of corrosion activity in real time, it has been possible to correlate specific changes in process conditions with corrosion events. Data from the electrochemical noise probe installed at the Simonette facility has been correlated to process changes as well as used to evaluate the chemical program in the stabilizer system, including measurement of the inhibitor film persistency. The electrochemical noise data was also used to define the effect of producing acid treatment fluids from wells into the battery on corrosion in the stabilizer system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".